This invention relates in general to the field of wireless communications, and more particularly to development of smart antennas for code division multiple access wireless communications.
Most of beam-forming techniques have been designed for Global Systems for Mobile communications (GSM) and time-division multiple access (TDMA)-based cellular systems described in articles by: M. Viberg et al., xe2x80x9cSensor Array Processing Based on Subspace Fitting,xe2x80x9d IEEE Trans. on Acoustics, Speech, Signal Processing, vol. ASSP-39, no. 5, pp. 1110-1121, May 1991; M. Taferner et al., xe2x80x9cA Novel DOA-Based Beamforming Algorithm with Broad Nulls,xe2x80x9d International Symposium on Personal, Indoor and Mobile Radio Communication""99, Osaka, Japan, September 1999; A. Kuchar and et al., xe2x80x9cReal-Time Smart Antenna Processing for GSM1800 Base Station,xe2x80x9d IEEE Vehicular Technology Conference""99, Houston, Tex., May 16-20, 1999; A. Kuchar et al., xe2x80x9cA Robust DOA-based Smart Antenna Processing for GSM Base Stations,xe2x80x9d IEEE International Conference on Communications""99, Vancouver, Jun. 6-10, 1999; J. H. Winters, xe2x80x9cSignal Acquisition and Tracking with Adaptive Arrays in Digital Mobile Radio Systems IS-54 with Flat Fading,xe2x80x9d IEEE Transactions on Vehicular Technology, vol. VT-42, no. 4, pp. 377-384, July 1993; J. H. Winters et al., xe2x80x9cThe Impact of Antenna Diversity on the Capacity of Wireless Communication Systems,xe2x80x9d IEEE Transactions on Communications, vol. COM-41, no. 4, pp. 1740-1751, April 1994; J. Razavilar et al., xe2x80x9cSoftware Radio Architecture with Smart Antennas: A Tutorial on Algorithms and Complexity,xe2x80x9d IEEE Journal on Selected Areas in Communications, vol. 17, no. 4, pp. 662-676, April 1999; S. Anderson et al., xe2x80x9cAn Adaptive Array for Mobile Communication Systems,xe2x80x9d IEEE Transactions on Vehicular Technology, vol. VT-40 (1), pp. 230-236, February 1991; S. Talwar et al., xe2x80x9cBlind Estimation of Multiple Co-Channel Digital Signals Using an Antenna Array,xe2x80x9d IEEE Signal Processing Letters, vol. (1)-2, pp. 29-31, February 1994; and L. Tong et al., xe2x80x9cWaveform-Preserving Blind Estimation of Multiple Independent Sources,xe2x80x9d IEEE Transactions on Acoustics, Speech, Signal Processing, vol. ASSP-41, no. 7, pp. 2461-2470, July 1993. The beam-forming techniques are not suitable for direct sequence (DS) code division multiple access (CDMA) systems because of the following reasons. First, all users in a CDMA wireless system are co-channel and their number could easily exceed the number of antennas. Moreover, due to multi-path propagation and the fact that each transmission path may contain direct, reflected and diffracted paths at different time delays, the array manifold may be poorly defined. Therefore, direction-finding-based beam-forming techniques may be difficult to apply. Also, no training or reference signals are present in the mobile to the base station link. Therefore, reference-signal based techniques cannot be used.
A smart antenna is defined to be an adaptive antenna array with a blind technique. It does not require any training signals or prior spatial information. Recently, a technique for estimating the vector channel and the corresponding adaptive beam-former has been developed for CDMA wireless systems as described in articles by: Ayman F. Naguib et al., xe2x80x9cPerformance of Wireless CDMA with M-ary Orthogonal Modulation and Cell Site Antenna Arrays,xe2x80x9d IEEE Journal on Selected Areas in Communications, Vol. 14, No. 9, pp. 1770-1783, December 1996; and Arogyaswami J. Paulraj et al., xe2x80x9cSpace-Time Modems for Wireless Personal Communications,xe2x80x9d IEEE Personal Communications, Vol. 5, No. 1, pp. 36-48, February 1998. In this technique, a code-filtering method is performed at each antenna for every finger (a parallel receiver to isolate multi-path components from the desired user in the system). The eigenstructure of the pre- and post-correlation array covariance matrices is used to estimate the channel vector and derive the corresponding adaptive beam-former. This technique was extended to the case of multi-path propagation using RAKE finger. The resulting overall receiver structure is called Beam-former-RAKE. The Beam-former-RAKE is a blind technique since it does not require any training signals, although it does assume the perfect knowledge of the spreading code for each finger of each user. It does not require any assumptions on the signal propagation and is, therefore, suitable for different propagation settings.
When the signal environment frequently changes because of the desired and undesired non-stationary signals, the adaptive beam-former must continuously update the weight vector to match the changing environment. The adaptive algorithms in the article by Ayman F. Naguib et al. are based on generalized eigenvector and an eigenvalue finding method, and are designed to maximize signal-to-interference-plus-noise output power ratio (SINR0). Although the smart antennas in the article by Ayman F. Naguib et al. show significant improvement in bit error rate (BER) performance compared to other existing smart antennas, they require enormous amounts of computation, and have not been simple to apply in practical fields. These heavy computations are due to the calculations of eigenvalues and eigenvectors of Mxc3x97M auto-covariance matrices for the antenna array outputs.
As mentioned in an article by Maggie Dunham et. al., xe2x80x9cTetherless T3 and Beyondxe2x80x9d, Interim Report, National Science Foundation Workshop on Nov. 19-20, 1998 (Available at the URL:http://www.cudenver.edu/public/engineer/T3-Workshop/T3Report-12-98.html) for tetherless T3 and beyond wireless communications, xe2x80x9cFast Protocols/Algorithmsxe2x80x9d are needed for xe2x80x9cTemporally-Spatially Varying Channels.xe2x80x9d Simple smart antennas based on maximum output power instead of maximum SINR0 were introduced in articles by D. Shim et al., xe2x80x9cA New Blind Adaptive Algorithm Based on Lagrange""s Formula for a Smart Antenna System in CDMA Mobile Communications,xe2x80x9d IEEE Vehicular Technology Conference, pp. 1160-1664, Ottawa, May 1998; Yoo S. Song et al., xe2x80x9cSimple Analysis of a Simple Smart Antenna for CDMA Wireless Communications,xe2x80x9d IEEE Vehicular Technology Conference, Houston, Tex., pp. 254-258, May 16-20, 1999; and Yoo S. Song et al., xe2x80x9cAnalysis of a Simple Smart Antenna for Code Division Multiple Access Wireless Communications,xe2x80x9d submitted to the IEEE Journal on Selected Area in Communications, June 1999, to significantly reduce the number of computations. The research in the article by D. Shim et al. shows performance similar to results in articles Yoo S. Song et al. However, the maximum output power criteria in the article by D. Shim et al. employs a Lagrange multiplier method and introduces slightly higher computational loads (5.5M compared to 4M in the articles by Yoo S. Song et al.). The maximum output power criteria may yield an adaptive and effective antenna weighting vector if the spread spectrum processing gain is high enough (e.g., 21 dB in the IS-95 CDMA systems). The received multi-path strength may not be equal in practice. A weak point of these algorithms are that the optimum weight vector for a weak path signal can track an undesired user or strong path signal direction if the power of the undesired signal after PN de-spreading is strong or if signal-to-interference input power ratio (SIRi) is low.
In a future CDMA wireless communications system low SIRi operation is more important than high SIRi. It is desirable to develop a smart antenna algorithm, which not only maximizes the SINR but also has smaller computation loads. In this invention two such smart antenna algorithms are invented and compared with the existing smart antenna with eignevector finding in the article by Ayman F. Naguib et al. and one based on the maximum output power criteria in the articles by Yoo S. Song et al. Two of four algorithms only require computational loads of the order 4M per snapshot where M is the number of antennas in a sector at a base station. The other invented algorithm requires computational loads of order 4M+2M2, and is based on the maximum SINR0 criteria. All three algorithms do not require any computation of eigenvalue and eigenvectors. All three smart antenna weight vectors are applied after post-PN processing as in the article by Ayman F. Naguib et al. to exploit the advantage of the DS-CDMA system over other time division multiple access (TDMA) or frequency division multiple access (FDMA) systems. Bit error rate (BER) versus the number of users are analyzed and simulated.
Cross pseudo-noise (PN) spreading and de-spreading and pilot-aided channel estimation in the cdma2000 reverse link are some of major different characteristics from the IS-95 code division multiple access (CDMA) wireless communications systems. These different features are included in this invention. Then, three simple smart antenna algorithms without eigenvector findings are presented for future high-speed high performed low-cost direct-sequence CDMA wireless communications systems, and compared with a conventional smart antenna with an eigenvector finding. Two only require computational loads of the order 4M per snapshot where M is the number of antennas in a sector at a base station. The other algorithm requires computational loads of order 4M+2M2. Two of them are based on the maximum signal-to-interference-plus-noise output power ratio (SINR0) criteria as the conventional algorithm, and one is based on the maximum output power criteria. All three simple smart antennas are for temporal and spatial varying channels. The conventional smart antennas require order larger than M2 due to generalized eigenvector finding and are difficult and costly to implement. Both equal and unequal strength Jake fading channels are employed. Both a scattered and a cluster interference model are considered. The bit (code symbol) error rate (BER) of the CDMA systems with the smart antennas are analyzed, simulated, and compared with those of the existing one. It is observed that the two simple smart antennas of order 4M may perform better than the existing one under unequal strength fading environment and/or a cluster of interfering users. In general, the other simple smart antenna algorithm of order 2M2+4M based on the maximum SINR criteria without eigenvector finding shows the best performance out of four smart antennas considered. Appendix provides the MATLAB program source codes, which were used to verify the invention claims.